Instructions to use EdisonScientific/Ibis0-BED-Simulator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EdisonScientific/Ibis0-BED-Simulator with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="EdisonScientific/Ibis0-BED-Simulator")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EdisonScientific/Ibis0-BED-Simulator")
model = AutoModelForCausalLM.from_pretrained("EdisonScientific/Ibis0-BED-Simulator", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EdisonScientific/Ibis0-BED-Simulator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EdisonScientific/Ibis0-BED-Simulator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EdisonScientific/Ibis0-BED-Simulator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EdisonScientific/Ibis0-BED-Simulator

SGLang

How to use EdisonScientific/Ibis0-BED-Simulator with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "EdisonScientific/Ibis0-BED-Simulator" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EdisonScientific/Ibis0-BED-Simulator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "EdisonScientific/Ibis0-BED-Simulator" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EdisonScientific/Ibis0-BED-Simulator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use EdisonScientific/Ibis0-BED-Simulator with Docker Model Runner:
```
docker model run hf.co/EdisonScientific/Ibis0-BED-Simulator
```

Ibis0 BED outcome simulator

paper · code · data

The outcome simulator from the Bayesian experimental design (BED) experiments in "Do Language Models Know What Compelling Scientific Evidence Looks Like?". Given a candidate experiment with its result hidden, the hypothesis θ′ that the experiment itself tests, and a requested valence, it generates a plausible outcome: either compelling support for θ′ or a clear null. During experiment selection the two outcomes are weighted by the belief model's p(θ′ | evidence) to estimate expected information gain. The belief model is Ibis0-30B-A3B-Classifier.

Clone and install ibis0

The prompt builder and BED scripts below live in the EdisonScientific/ibis0 code repository. Install Python 3.12 and uv, then clone and install the repository:

git clone --recurse-submodules https://github.com/EdisonScientific/ibis0.git
cd ibis0
uv sync --frozen --all-groups

Run the referenced scripts from this repository with uv run python <script-path>. Local GPU inference requires Linux and a CUDA GPU.

Use

build_theta_prime_sft.py builds the simulator's prompts from an experiment, its hypothesis, and the requested outcome type. bed_thetaprime.py uses the simulator and classifier together to select experiments.

The simulator returns JSON with three fields: supports_hypothesis ("yes" or "no"), weight_of_evidence_decibans, and y, which contains the simulated measurements for the domain.

Licence

Apache-2.0. Fine-tuned from Qwen/Qwen3-30B-A3B (Apache-2.0).

Citation

@article{ibis0,
  title   = {Do Language Models Know What Compelling Scientific Evidence Looks Like?},
  author  = {Igoe, Conor and Grand, Gabriel and Koch, Zane and Bou, Albert and Moss, Eli and Hinks, Michaela M. and Rodriques, Samuel G. and White, Andrew and Narayanan, Siddharth},
  journal = {arXiv preprint arXiv:ARXIV_ID},
  year    = {2026}
}